Understanding factors influencing AI adoption in education: Insights from a Meta-Analytic Structural Equation Modelling study

Abstract

The rapid integration of Artificial Intelligence in Education (AIED) transformed teaching and learning processes. The study employed the Technology Acceptance Model (TAM) to analyse factors influencing the acceptance of AI tools in educational settings. By utilising One-step Meta-analytic Structural Equation Modelling (OSMASEM), findings from 17 empirical studies were synthesised to explore the relationships among TAM constructs—Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude Towards Use (ATU), and Intention to Use (ITU)—in the context of AIED. The analysis revealed significant direct and indirect effects, with PEOU strongly influencing PU and both PEOU and PU positively affecting ATU and ITU. The results highlighted TAM’s robustness and applicability in predicting technology acceptance behaviours in education, highlighting the critical roles of usability and perceived benefits in driving AI adoption. The findings provided valuable insights for educators, policymakers, and developers aiming to enhance AI integration in education, emphasising the importance of designing user-friendly and beneficial AI tools to foster positive attitudes and increased usage intentions among educators and students.